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+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_LLT_H
+#define EIGEN_LLT_H
+
+namespace Eigen {
+
+namespace internal{
+template<typename MatrixType, int UpLo> struct LLT_Traits;
+}
+
+/** \ingroup Cholesky_Module
+ *
+ * \class LLT
+ *
+ * \brief Standard Cholesky decomposition (LL^T) of a matrix and associated features
+ *
+ * \tparam _MatrixType the type of the matrix of which we are computing the LL^T Cholesky decomposition
+ * \tparam _UpLo the triangular part that will be used for the decompositon: Lower (default) or Upper.
+ * The other triangular part won't be read.
+ *
+ * This class performs a LL^T Cholesky decomposition of a symmetric, positive definite
+ * matrix A such that A = LL^* = U^*U, where L is lower triangular.
+ *
+ * While the Cholesky decomposition is particularly useful to solve selfadjoint problems like D^*D x = b,
+ * for that purpose, we recommend the Cholesky decomposition without square root which is more stable
+ * and even faster. Nevertheless, this standard Cholesky decomposition remains useful in many other
+ * situations like generalised eigen problems with hermitian matrices.
+ *
+ * Remember that Cholesky decompositions are not rank-revealing. This LLT decomposition is only stable on positive definite matrices,
+ * use LDLT instead for the semidefinite case. Also, do not use a Cholesky decomposition to determine whether a system of equations
+ * has a solution.
+ *
+ * Example: \include LLT_example.cpp
+ * Output: \verbinclude LLT_example.out
+ *
+ * This class supports the \link InplaceDecomposition inplace decomposition \endlink mechanism.
+ *
+ * \sa MatrixBase::llt(), SelfAdjointView::llt(), class LDLT
+ */
+ /* HEY THIS DOX IS DISABLED BECAUSE THERE's A BUG EITHER HERE OR IN LDLT ABOUT THAT (OR BOTH)
+ * Note that during the decomposition, only the upper triangular part of A is considered. Therefore,
+ * the strict lower part does not have to store correct values.
+ */
+template<typename _MatrixType, int _UpLo> class LLT
+{
+ public:
+ typedef _MatrixType MatrixType;
+ enum {
+ RowsAtCompileTime = MatrixType::RowsAtCompileTime,
+ ColsAtCompileTime = MatrixType::ColsAtCompileTime,
+ MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime
+ };
+ typedef typename MatrixType::Scalar Scalar;
+ typedef typename NumTraits<typename MatrixType::Scalar>::Real RealScalar;
+ typedef Eigen::Index Index; ///< \deprecated since Eigen 3.3
+ typedef typename MatrixType::StorageIndex StorageIndex;
+
+ enum {
+ PacketSize = internal::packet_traits<Scalar>::size,
+ AlignmentMask = int(PacketSize)-1,
+ UpLo = _UpLo
+ };
+
+ typedef internal::LLT_Traits<MatrixType,UpLo> Traits;
+
+ /**
+ * \brief Default Constructor.
+ *
+ * The default constructor is useful in cases in which the user intends to
+ * perform decompositions via LLT::compute(const MatrixType&).
+ */
+ LLT() : m_matrix(), m_isInitialized(false) {}
+
+ /** \brief Default Constructor with memory preallocation
+ *
+ * Like the default constructor but with preallocation of the internal data
+ * according to the specified problem \a size.
+ * \sa LLT()
+ */
+ explicit LLT(Index size) : m_matrix(size, size),
+ m_isInitialized(false) {}
+
+ template<typename InputType>
+ explicit LLT(const EigenBase<InputType>& matrix)
+ : m_matrix(matrix.rows(), matrix.cols()),
+ m_isInitialized(false)
+ {
+ compute(matrix.derived());
+ }
+
+ /** \brief Constructs a LDLT factorization from a given matrix
+ *
+ * This overloaded constructor is provided for \link InplaceDecomposition inplace decomposition \endlink when
+ * \c MatrixType is a Eigen::Ref.
+ *
+ * \sa LLT(const EigenBase&)
+ */
+ template<typename InputType>
+ explicit LLT(EigenBase<InputType>& matrix)
+ : m_matrix(matrix.derived()),
+ m_isInitialized(false)
+ {
+ compute(matrix.derived());
+ }
+
+ /** \returns a view of the upper triangular matrix U */
+ inline typename Traits::MatrixU matrixU() const
+ {
+ eigen_assert(m_isInitialized && "LLT is not initialized.");
+ return Traits::getU(m_matrix);
+ }
+
+ /** \returns a view of the lower triangular matrix L */
+ inline typename Traits::MatrixL matrixL() const
+ {
+ eigen_assert(m_isInitialized && "LLT is not initialized.");
+ return Traits::getL(m_matrix);
+ }
+
+ /** \returns the solution x of \f$ A x = b \f$ using the current decomposition of A.
+ *
+ * Since this LLT class assumes anyway that the matrix A is invertible, the solution
+ * theoretically exists and is unique regardless of b.
+ *
+ * Example: \include LLT_solve.cpp
+ * Output: \verbinclude LLT_solve.out
+ *
+ * \sa solveInPlace(), MatrixBase::llt(), SelfAdjointView::llt()
+ */
+ template<typename Rhs>
+ inline const Solve<LLT, Rhs>
+ solve(const MatrixBase<Rhs>& b) const
+ {
+ eigen_assert(m_isInitialized && "LLT is not initialized.");
+ eigen_assert(m_matrix.rows()==b.rows()
+ && "LLT::solve(): invalid number of rows of the right hand side matrix b");
+ return Solve<LLT, Rhs>(*this, b.derived());
+ }
+
+ template<typename Derived>
+ void solveInPlace(MatrixBase<Derived> &bAndX) const;
+
+ template<typename InputType>
+ LLT& compute(const EigenBase<InputType>& matrix);
+
+ /** \returns an estimate of the reciprocal condition number of the matrix of
+ * which \c *this is the Cholesky decomposition.
+ */
+ RealScalar rcond() const
+ {
+ eigen_assert(m_isInitialized && "LLT is not initialized.");
+ eigen_assert(m_info == Success && "LLT failed because matrix appears to be negative");
+ return internal::rcond_estimate_helper(m_l1_norm, *this);
+ }
+
+ /** \returns the LLT decomposition matrix
+ *
+ * TODO: document the storage layout
+ */
+ inline const MatrixType& matrixLLT() const
+ {
+ eigen_assert(m_isInitialized && "LLT is not initialized.");
+ return m_matrix;
+ }
+
+ MatrixType reconstructedMatrix() const;
+
+
+ /** \brief Reports whether previous computation was successful.
+ *
+ * \returns \c Success if computation was succesful,
+ * \c NumericalIssue if the matrix.appears to be negative.
+ */
+ ComputationInfo info() const
+ {
+ eigen_assert(m_isInitialized && "LLT is not initialized.");
+ return m_info;
+ }
+
+ /** \returns the adjoint of \c *this, that is, a const reference to the decomposition itself as the underlying matrix is self-adjoint.
+ *
+ * This method is provided for compatibility with other matrix decompositions, thus enabling generic code such as:
+ * \code x = decomposition.adjoint().solve(b) \endcode
+ */
+ const LLT& adjoint() const { return *this; };
+
+ inline Index rows() const { return m_matrix.rows(); }
+ inline Index cols() const { return m_matrix.cols(); }
+
+ template<typename VectorType>
+ LLT rankUpdate(const VectorType& vec, const RealScalar& sigma = 1);
+
+ #ifndef EIGEN_PARSED_BY_DOXYGEN
+ template<typename RhsType, typename DstType>
+ EIGEN_DEVICE_FUNC
+ void _solve_impl(const RhsType &rhs, DstType &dst) const;
+ #endif
+
+ protected:
+
+ static void check_template_parameters()
+ {
+ EIGEN_STATIC_ASSERT_NON_INTEGER(Scalar);
+ }
+
+ /** \internal
+ * Used to compute and store L
+ * The strict upper part is not used and even not initialized.
+ */
+ MatrixType m_matrix;
+ RealScalar m_l1_norm;
+ bool m_isInitialized;
+ ComputationInfo m_info;
+};
+
+namespace internal {
+
+template<typename Scalar, int UpLo> struct llt_inplace;
+
+template<typename MatrixType, typename VectorType>
+static Index llt_rank_update_lower(MatrixType& mat, const VectorType& vec, const typename MatrixType::RealScalar& sigma)
+{
+ using std::sqrt;
+ typedef typename MatrixType::Scalar Scalar;
+ typedef typename MatrixType::RealScalar RealScalar;
+ typedef typename MatrixType::ColXpr ColXpr;
+ typedef typename internal::remove_all<ColXpr>::type ColXprCleaned;
+ typedef typename ColXprCleaned::SegmentReturnType ColXprSegment;
+ typedef Matrix<Scalar,Dynamic,1> TempVectorType;
+ typedef typename TempVectorType::SegmentReturnType TempVecSegment;
+
+ Index n = mat.cols();
+ eigen_assert(mat.rows()==n && vec.size()==n);
+
+ TempVectorType temp;
+
+ if(sigma>0)
+ {
+ // This version is based on Givens rotations.
+ // It is faster than the other one below, but only works for updates,
+ // i.e., for sigma > 0
+ temp = sqrt(sigma) * vec;
+
+ for(Index i=0; i<n; ++i)
+ {
+ JacobiRotation<Scalar> g;
+ g.makeGivens(mat(i,i), -temp(i), &mat(i,i));
+
+ Index rs = n-i-1;
+ if(rs>0)
+ {
+ ColXprSegment x(mat.col(i).tail(rs));
+ TempVecSegment y(temp.tail(rs));
+ apply_rotation_in_the_plane(x, y, g);
+ }
+ }
+ }
+ else
+ {
+ temp = vec;
+ RealScalar beta = 1;
+ for(Index j=0; j<n; ++j)
+ {
+ RealScalar Ljj = numext::real(mat.coeff(j,j));
+ RealScalar dj = numext::abs2(Ljj);
+ Scalar wj = temp.coeff(j);
+ RealScalar swj2 = sigma*numext::abs2(wj);
+ RealScalar gamma = dj*beta + swj2;
+
+ RealScalar x = dj + swj2/beta;
+ if (x<=RealScalar(0))
+ return j;
+ RealScalar nLjj = sqrt(x);
+ mat.coeffRef(j,j) = nLjj;
+ beta += swj2/dj;
+
+ // Update the terms of L
+ Index rs = n-j-1;
+ if(rs)
+ {
+ temp.tail(rs) -= (wj/Ljj) * mat.col(j).tail(rs);
+ if(gamma != 0)
+ mat.col(j).tail(rs) = (nLjj/Ljj) * mat.col(j).tail(rs) + (nLjj * sigma*numext::conj(wj)/gamma)*temp.tail(rs);
+ }
+ }
+ }
+ return -1;
+}
+
+template<typename Scalar> struct llt_inplace<Scalar, Lower>
+{
+ typedef typename NumTraits<Scalar>::Real RealScalar;
+ template<typename MatrixType>
+ static Index unblocked(MatrixType& mat)
+ {
+ using std::sqrt;
+
+ eigen_assert(mat.rows()==mat.cols());
+ const Index size = mat.rows();
+ for(Index k = 0; k < size; ++k)
+ {
+ Index rs = size-k-1; // remaining size
+
+ Block<MatrixType,Dynamic,1> A21(mat,k+1,k,rs,1);
+ Block<MatrixType,1,Dynamic> A10(mat,k,0,1,k);
+ Block<MatrixType,Dynamic,Dynamic> A20(mat,k+1,0,rs,k);
+
+ RealScalar x = numext::real(mat.coeff(k,k));
+ if (k>0) x -= A10.squaredNorm();
+ if (x<=RealScalar(0))
+ return k;
+ mat.coeffRef(k,k) = x = sqrt(x);
+ if (k>0 && rs>0) A21.noalias() -= A20 * A10.adjoint();
+ if (rs>0) A21 /= x;
+ }
+ return -1;
+ }
+
+ template<typename MatrixType>
+ static Index blocked(MatrixType& m)
+ {
+ eigen_assert(m.rows()==m.cols());
+ Index size = m.rows();
+ if(size<32)
+ return unblocked(m);
+
+ Index blockSize = size/8;
+ blockSize = (blockSize/16)*16;
+ blockSize = (std::min)((std::max)(blockSize,Index(8)), Index(128));
+
+ for (Index k=0; k<size; k+=blockSize)
+ {
+ // partition the matrix:
+ // A00 | - | -
+ // lu = A10 | A11 | -
+ // A20 | A21 | A22
+ Index bs = (std::min)(blockSize, size-k);
+ Index rs = size - k - bs;
+ Block<MatrixType,Dynamic,Dynamic> A11(m,k, k, bs,bs);
+ Block<MatrixType,Dynamic,Dynamic> A21(m,k+bs,k, rs,bs);
+ Block<MatrixType,Dynamic,Dynamic> A22(m,k+bs,k+bs,rs,rs);
+
+ Index ret;
+ if((ret=unblocked(A11))>=0) return k+ret;
+ if(rs>0) A11.adjoint().template triangularView<Upper>().template solveInPlace<OnTheRight>(A21);
+ if(rs>0) A22.template selfadjointView<Lower>().rankUpdate(A21,typename NumTraits<RealScalar>::Literal(-1)); // bottleneck
+ }
+ return -1;
+ }
+
+ template<typename MatrixType, typename VectorType>
+ static Index rankUpdate(MatrixType& mat, const VectorType& vec, const RealScalar& sigma)
+ {
+ return Eigen::internal::llt_rank_update_lower(mat, vec, sigma);
+ }
+};
+
+template<typename Scalar> struct llt_inplace<Scalar, Upper>
+{
+ typedef typename NumTraits<Scalar>::Real RealScalar;
+
+ template<typename MatrixType>
+ static EIGEN_STRONG_INLINE Index unblocked(MatrixType& mat)
+ {
+ Transpose<MatrixType> matt(mat);
+ return llt_inplace<Scalar, Lower>::unblocked(matt);
+ }
+ template<typename MatrixType>
+ static EIGEN_STRONG_INLINE Index blocked(MatrixType& mat)
+ {
+ Transpose<MatrixType> matt(mat);
+ return llt_inplace<Scalar, Lower>::blocked(matt);
+ }
+ template<typename MatrixType, typename VectorType>
+ static Index rankUpdate(MatrixType& mat, const VectorType& vec, const RealScalar& sigma)
+ {
+ Transpose<MatrixType> matt(mat);
+ return llt_inplace<Scalar, Lower>::rankUpdate(matt, vec.conjugate(), sigma);
+ }
+};
+
+template<typename MatrixType> struct LLT_Traits<MatrixType,Lower>
+{
+ typedef const TriangularView<const MatrixType, Lower> MatrixL;
+ typedef const TriangularView<const typename MatrixType::AdjointReturnType, Upper> MatrixU;
+ static inline MatrixL getL(const MatrixType& m) { return MatrixL(m); }
+ static inline MatrixU getU(const MatrixType& m) { return MatrixU(m.adjoint()); }
+ static bool inplace_decomposition(MatrixType& m)
+ { return llt_inplace<typename MatrixType::Scalar, Lower>::blocked(m)==-1; }
+};
+
+template<typename MatrixType> struct LLT_Traits<MatrixType,Upper>
+{
+ typedef const TriangularView<const typename MatrixType::AdjointReturnType, Lower> MatrixL;
+ typedef const TriangularView<const MatrixType, Upper> MatrixU;
+ static inline MatrixL getL(const MatrixType& m) { return MatrixL(m.adjoint()); }
+ static inline MatrixU getU(const MatrixType& m) { return MatrixU(m); }
+ static bool inplace_decomposition(MatrixType& m)
+ { return llt_inplace<typename MatrixType::Scalar, Upper>::blocked(m)==-1; }
+};
+
+} // end namespace internal
+
+/** Computes / recomputes the Cholesky decomposition A = LL^* = U^*U of \a matrix
+ *
+ * \returns a reference to *this
+ *
+ * Example: \include TutorialLinAlgComputeTwice.cpp
+ * Output: \verbinclude TutorialLinAlgComputeTwice.out
+ */
+template<typename MatrixType, int _UpLo>
+template<typename InputType>
+LLT<MatrixType,_UpLo>& LLT<MatrixType,_UpLo>::compute(const EigenBase<InputType>& a)
+{
+ check_template_parameters();
+
+ eigen_assert(a.rows()==a.cols());
+ const Index size = a.rows();
+ m_matrix.resize(size, size);
+ m_matrix = a.derived();
+
+ // Compute matrix L1 norm = max abs column sum.
+ m_l1_norm = RealScalar(0);
+ // TODO move this code to SelfAdjointView
+ for (Index col = 0; col < size; ++col) {
+ RealScalar abs_col_sum;
+ if (_UpLo == Lower)
+ abs_col_sum = m_matrix.col(col).tail(size - col).template lpNorm<1>() + m_matrix.row(col).head(col).template lpNorm<1>();
+ else
+ abs_col_sum = m_matrix.col(col).head(col).template lpNorm<1>() + m_matrix.row(col).tail(size - col).template lpNorm<1>();
+ if (abs_col_sum > m_l1_norm)
+ m_l1_norm = abs_col_sum;
+ }
+
+ m_isInitialized = true;
+ bool ok = Traits::inplace_decomposition(m_matrix);
+ m_info = ok ? Success : NumericalIssue;
+
+ return *this;
+}
+
+/** Performs a rank one update (or dowdate) of the current decomposition.
+ * If A = LL^* before the rank one update,
+ * then after it we have LL^* = A + sigma * v v^* where \a v must be a vector
+ * of same dimension.
+ */
+template<typename _MatrixType, int _UpLo>
+template<typename VectorType>
+LLT<_MatrixType,_UpLo> LLT<_MatrixType,_UpLo>::rankUpdate(const VectorType& v, const RealScalar& sigma)
+{
+ EIGEN_STATIC_ASSERT_VECTOR_ONLY(VectorType);
+ eigen_assert(v.size()==m_matrix.cols());
+ eigen_assert(m_isInitialized);
+ if(internal::llt_inplace<typename MatrixType::Scalar, UpLo>::rankUpdate(m_matrix,v,sigma)>=0)
+ m_info = NumericalIssue;
+ else
+ m_info = Success;
+
+ return *this;
+}
+
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+template<typename _MatrixType,int _UpLo>
+template<typename RhsType, typename DstType>
+void LLT<_MatrixType,_UpLo>::_solve_impl(const RhsType &rhs, DstType &dst) const
+{
+ dst = rhs;
+ solveInPlace(dst);
+}
+#endif
+
+/** \internal use x = llt_object.solve(x);
+ *
+ * This is the \em in-place version of solve().
+ *
+ * \param bAndX represents both the right-hand side matrix b and result x.
+ *
+ * This version avoids a copy when the right hand side matrix b is not needed anymore.
+ *
+ * \sa LLT::solve(), MatrixBase::llt()
+ */
+template<typename MatrixType, int _UpLo>
+template<typename Derived>
+void LLT<MatrixType,_UpLo>::solveInPlace(MatrixBase<Derived> &bAndX) const
+{
+ eigen_assert(m_isInitialized && "LLT is not initialized.");
+ eigen_assert(m_matrix.rows()==bAndX.rows());
+ matrixL().solveInPlace(bAndX);
+ matrixU().solveInPlace(bAndX);
+}
+
+/** \returns the matrix represented by the decomposition,
+ * i.e., it returns the product: L L^*.
+ * This function is provided for debug purpose. */
+template<typename MatrixType, int _UpLo>
+MatrixType LLT<MatrixType,_UpLo>::reconstructedMatrix() const
+{
+ eigen_assert(m_isInitialized && "LLT is not initialized.");
+ return matrixL() * matrixL().adjoint().toDenseMatrix();
+}
+
+/** \cholesky_module
+ * \returns the LLT decomposition of \c *this
+ * \sa SelfAdjointView::llt()
+ */
+template<typename Derived>
+inline const LLT<typename MatrixBase<Derived>::PlainObject>
+MatrixBase<Derived>::llt() const
+{
+ return LLT<PlainObject>(derived());
+}
+
+/** \cholesky_module
+ * \returns the LLT decomposition of \c *this
+ * \sa SelfAdjointView::llt()
+ */
+template<typename MatrixType, unsigned int UpLo>
+inline const LLT<typename SelfAdjointView<MatrixType, UpLo>::PlainObject, UpLo>
+SelfAdjointView<MatrixType, UpLo>::llt() const
+{
+ return LLT<PlainObject,UpLo>(m_matrix);
+}
+
+} // end namespace Eigen
+
+#endif // EIGEN_LLT_H